Improving Driving Safety by Detecting Negative Emotions with Biological Signals: Which Is the Best?

2021 ◽  
pp. 036119812110415
Author(s):  
Naser Habibifar ◽  
Hamed Salmanzadeh
Keyword(s):  
Driving Simulator ◽  
Electrodermal Activity ◽  
Negative Emotions ◽  
Evaluation Criteria ◽  
Driving Safety ◽  
Driver Assistance Systems ◽  
Ample Evidence ◽  
Biological Signals ◽  
Fear And Anxiety ◽  
Sensitivity Specificity

There is ample evidence confirming the adverse effects of negative emotions such as anger, fear, and anxiety on drivers’ performance. Also, effectiveness of biological signals in emotion recognition has been confirmed. Therefore, developing advanced driver-assistance systems based on biological signals to detect negative emotions can play a major role in improving driving safety. However, since recording signals, data analysis, as well as design and implementation of a system based on one or more biological signals take time and are costly, it is necessary to conduct appropriate preliminary studies on the efficiency of these signals in identifying negative emotions. The purpose of this study was to explore the efficiency of four biological signals including electrocardiogram (ECG), electromyogram (EMG), electrodermal activity (EDA), and electroencephalogram (EEG) in detecting negative emotions while driving. To this end, a series of scenarios were designed to arouse negative emotions in the driving simulator environment. A total of 43 individuals participated in the experiments, during which the four signals were recorded. Next, we extracted 58 features from the collected data for analysis. Then, multi-layer perceptron and radial basis function neural networks were implemented using the features of each of these signals separately. Afterward, the four evaluation criteria of accuracy, sensitivity, specificity, and precision were calculated for the signals. Finally, TOPSIS was used to rank the signals. ECG and EDA signals, with 88% and 90% accuracy, respectively, were found to be the best signals in detecting negative emotions during driving.

scholarly journals A Recognition Model of Driving Risk Based on Belief Rule-Base Methodology

2018 ◽  
Vol 32 (11) ◽  
pp. 1850037 ◽  
Author(s):  
Chuan Sun ◽  
Chaozhong Wu ◽  
Duanfeng Chu ◽  
Zhenji Lu ◽  
Jian Tan ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Data Driven ◽  
Driving Safety ◽  
Rule Base ◽  
Driver Assistance Systems ◽  
Recognition Model ◽  
Time Operation ◽  
Belief Rule Base ◽  
Driving Risk ◽  
Data Driven Modeling

This paper aims to recognize driving risks in individual vehicles online based on a data-driven methodology. Existing advanced driver assistance systems (ADAS) have difficulties in effectively processing multi-source heterogeneous driving data. Furthermore, parameters adopted for evaluating the driving risk are limited in these systems. The approach of data-driven modeling is investigated in this study for utilizing the accumulation of on-road driving data. A recognition model of driving risk based on belief rule-base (BRB) methodology is built, predicting driving safety as a function of driver characteristics, vehicle state and road environment conditions. The BRB model was calibrated and validated using on-road data from 30 drivers. The test results show that the recognition accuracy of our proposed model can reach about 90% in all situations with three levels (none, medium, large) of driving risks. Furthermore, the proposed simplified model, which provides real-time operation, is implemented in a vehicle driving simulator as a reference for future ADAS and belongs to research on artificial intelligence (AI) in the automotive field.

Driving Simulator System to Evaluate Driver’s Workload Using ADAS in Different Driving Contexts

2017 ◽  
Author(s):  
Giandomenico Caruso ◽  
Daniele Ruscio ◽  
Dedy Ariansyah ◽  
Monica Bordegoni
Keyword(s):  
Driving Simulator ◽  
Driving Simulation ◽  
Human Cognition ◽  
Driving Safety ◽  
Physiological Data ◽  
Driver Assistance Systems ◽  
Safe Driving ◽  
Dangerous Driving ◽  
Vehicle Technology ◽  
Physiological Sensors

The advancement of in-vehicle technology for driving safety has considerably improved. Current Advanced Driver-Assistance Systems (ADAS) make road safer by alerting the driver, through visual, auditory, and haptic signals about dangerous driving situations, and consequently, preventing possible collisions. However, in some circumstances the driver can fail to properly respond to the alert since human cognition systems can be influenced by the driving context. Driving simulation can help evaluating this aspect since it is possible to reproduce different ADAS in safe driving conditions. However, driving simulation alone does not provide information about how the change in driver’s workload affects the interaction of the driver with ADAS. This paper presents a driving simulator system integrating physiological sensors that acquire heart’s activity, blood volume pulse, respiration rate, and skin conductance parameters. Through a specific processing of these measurements, it is possible to measure different cognitive processes that contribute to the change of driver’s workload while using ADAS, in different driving contexts. The preliminary studies conducted in this research show the effectiveness of this system and provide guidelines for the future acquisition and the treatment of the physiological data to assess ADAS workload.

Researches on Adaptive Cruise Control system: A state of the art review

2021 ◽  
pp. 095440702110192
Author(s):  
Liangyao Yu ◽  
Ruyue Wang
Keyword(s):  
Adaptive Cruise Control ◽  
Driving Safety ◽  
Cruise Control ◽  
Driver Assistance Systems ◽  
Human Machine Interaction ◽  
System A ◽  
Cruise Control System ◽  
Preceding Vehicle ◽  
Energy Economy ◽  
Machine Interaction

Adaptive Cruise Control (ACC) is one of Advanced Driver Assistance Systems (ADAS) which takes over vehicle longitudinal control under necessary driving scenarios. Vehicle in ACC mode automatically adjusts speed to follow the preceding vehicle based on evaluation of the surrounding traffic. ACC reduces drivers’ workload as well as improves driving safety, energy economy, and traffic flow. This article provides a comprehensive review of the researches on ACC. Firstly, an overview of ACC controller and applied control theories are introduced. Their principles and performances are discussed. Secondly, several application cases of ACC control algorithms are presented. Then validation work including simulation, Hardware-in-the-Loop (HiL) test and on-road experiment is descripted to provide ideas for testing ACC systems for different aims and fidelities. In addition, studies on human-machine interaction are also summarized in this review to provide insights on development of ACC from the perspective of users. At last, challenges and potential directions in this field is discussed, including consideration of vehicle dynamics properties, contradiction between algorithm performance and computation as well as integration of ACC to other intelligent functions on vehicles.

scholarly journals Risk Assessment by a Passenger of an Autonomous Vehicle Among Pedestrians: Relationship Between Subjective and Physiological Measures

2021 ◽  
Vol 2 ◽  
Author(s):  
Jeffery Petit ◽  
Camilo Charron ◽  
Franck Mars
Keyword(s):  
Risk Perception ◽  
Autonomous Navigation ◽  
Driving Simulator ◽  
Electrodermal Activity ◽  
Autonomous Vehicle ◽  
Information Criterion ◽  
Redundant Data ◽  
Safety Margins ◽  
Subjective Risk ◽  
Two Systems

Autonomous navigation becomes complex when it is performed in an environment that lacks road signs and includes a variety of users, including vulnerable pedestrians. This article deals with the perception of collision risk from the viewpoint of a passenger sitting in the driver's seat who has delegated the total control of their vehicle to an autonomous system. The proposed study is based on an experiment that used a fixed-base driving simulator. The study was conducted using a group of 20 volunteer participants. Scenarios were developed to simulate avoidance manoeuvres that involved pedestrians walking at 4.5 kph and an autonomous vehicle that was otherwise driving in a straight line at 30 kph. The main objective was to compare two systems of risk perception: These included subjective risk assessments obtained with an analogue handset provided to the participants and electrodermal activity (EDA) that was measured using skin conductance sensors. The relationship between these two types of measures, which possibly relates to the two systems of risk perception, is not unequivocally described in the literature. This experiment addresses this relationship by manipulating two factors: The time-to-collision (TTC) at the initiation of a pedestrian avoidance manoeuvre and the lateral offset left between a vehicle and a pedestrian. These manipulations of vehicle dynamics made it possible to simulate different safety margins regarding pedestrians during avoidance manoeuvres. The conditional dependencies between the two systems and the manipulated factors were studied using hybrid Bayesian networks. This relationship was inferred by selecting the best Bayesian network structure based on the Bayesian information criterion. The results demonstrate that the reduction of safety margins increases risk perception according to both types of indicators. However, the increase in subjective risk is more pronounced than the physiological response. While the indicators cannot be considered redundant, data modeling suggests that the two risk perception systems are not independent.

Physiological Indicators of Driver Workload During Car-Following Scenarios and Takeovers in Highly Automated Driving

2021 ◽  
Author(s):  
Vishnu Radhakrishnan ◽  
Natasha Merat ◽  
Tyron Louw ◽  
Rafael Goncalves ◽  
Wei Lyu ◽  
...  
Keyword(s):  
Driving Simulator ◽  
Electrodermal Activity ◽  
Automated Driving ◽  
Physiological Indicators ◽  
Car Following ◽  
Time Headway ◽  
Driver Workload ◽  
Driving Task ◽  
Vehicle Automation ◽  
Lead Vehicle

This driving simulator study, conducted as a part of Horizon2020-funded L3Pilot project, investigated how different car-following situations affected driver workload, within the context of vehicle automation. Electrocardiogram (ECG) and electrodermal activity (EDA)-based physiological metrics were used as objective indicators of workload, along with self-reported workload ratings. A total of 32 drivers were divided into two equal groups, based on whether they engaged in a non-driving related task (NDRT) during automation or monitored the drive. Drivers in both groups were exposed to two counterbalanced experimental drives, lasting ~18 minutes each, of Short (0.5 s) and Long (1.5 s) Time Headway conditions during automated car-following (ACF), which was followed by a takeover that happened with or without a lead vehicle. We observed that the workload on the driver due to the NDRT was significantly higher than both monitoring the drive during ACF and manual car-following (MCF). Furthermore, the results indicated that shorter THWs and the presence of a lead vehicle can significantly increase driver workload during takeover scenarios, potentially affecting the safety of the vehicle. This warrants further research into understanding safe time headway thresholds to be maintained by automated vehicles, without placing additional mental or attentional demands on the driver. To conclude, our results indicated that ECG and EDA signals are sensitive to variations in workload, and hence, warrants further investigation on the value of combining these two signals to assess driver workload in real-time, to help the system respond appropriately to the limitations of the driver and predict their performance in driving task if and when they have to resume manual control of the vehicle.

Driving Home the Message

2014 ◽  
Vol 6 (1) ◽  
pp. 50-63 ◽  
Author(s):  
Edward Downs
Keyword(s):  
Video Game ◽  
Driving Simulator ◽  
Cognitive Architecture ◽  
Distracted Driving ◽  
Driving Safety ◽  
Control Group ◽  
Comparison Results ◽  
Post Test ◽  
Game Console ◽  
Original Procedure

A pre-test, post-test experiment was conducted to determine if using a popular racing game on a PlayStation® 3 video game console could change a player's intent to drive distracted. Results indicated that those who were driving distracted (texting or talking) in a video game driving simulator had significantly more crashes, speed violations, and fog-line crossings than those in a non-distracted driving control group. These findings are consistent with predictions from the ACT-R cognitive architecture and threaded cognition theory. A follow-up study manipulated the original protocol by establishing a non-distracted baseline for participants' driving abilities as a comparison. Results demonstrated that this manipulation resulted in a significantly stronger change in attitude against driving distracted than in the original procedure. The implications help to inform driving safety programs on proper protocol for the use of game consoles to change attitudes toward distracted driving.

Minding the Gap: Effects of an Attention Maintenance Training Program on Driver Calibration

2020 ◽  
pp. 001872082096529
Author(s):  
James Unverricht ◽  
Yusuke Yamani ◽  
Jing Chen ◽  
William J. Horrey
Keyword(s):  
Training Program ◽  
Cognitive Skills ◽  
Driving Simulator ◽  
Training Programs ◽  
Driving Safety ◽  
Driver Training ◽  
Actual Performance ◽  
Young Drivers ◽  
Maintenance Behaviors ◽  
Target Skills

Objective The present study examines the effect of an existing driver training program, FOrward Concentration and Attention Learning (FOCAL) on young drivers’ calibration, drivers’ ability to estimate the length of their in-vehicle glances while driving, using two different measures, normalized difference scores and Brier Scores. Background Young drivers are poor at maintaining attention to the forward roadway while driving a vehicle. Additionally, drivers may overestimate their attention maintenance abilities. Driver training programs such as FOCAL may train target skills such as attention maintenance but also might serve as a promising way to reduce errors in drivers’ calibration of their self-perceived attention maintenance behaviors in comparison to their actual performance. Method Thirty-six participants completed either FOCAL or a Placebo training program, immediately followed by driving simulator evaluations of their attention maintenance performance. In the evaluation drive, participants navigated four driving simulator scenarios during which their eyes were tracked. In each scenario, participants performed a map task on a tablet simulating an in-vehicle infotainment system. Results FOCAL-trained drivers maintained their attention to the forward roadway more and reported better calibration using the normalized difference measure than Placebo-trained drivers. However, the Brier scores did not distinguish the two groups on their calibration. Conclusion The study implies that FOCAL has the potential to improve not only attention maintenance skills but also calibration of the skills for young drivers. Application Driver training programs may be designed to train not only targeted higher cognitive skills but also driver calibration—both critical for driving safety in young drivers.

A motivational driver model for the design of a rear-end crash avoidance system

2019 ◽  
Vol 234 (1) ◽  
pp. 10-26
Author(s):  
Hamed Mozaffari ◽  
Ali Nahvi
Keyword(s):  
Driving Simulator ◽  
Human Subjects ◽  
Autonomous Driving ◽  
Driver Model ◽  
Driver Assistance Systems ◽  
Optimal Point ◽  
Time To Collision ◽  
Human Aspects ◽  
Crash Avoidance ◽  
Assistance Systems

A motivational driver model is developed to design a rear-end crash avoidance system. Current driver assistance systems use engineering methods without considering psychological human aspects, which leads to false activation of assistance systems and complicated control algorithms. The presented driver model estimates driver’s psychological motivations using the combined longitudinal and lateral time to collision, the vehicle kinematics, and the vehicle dynamics. These motivations simplify both autonomous driving algorithms and human-machine interactions. The optimal point of a motivational multi-objective cost function defines the decision for the autonomous driving. Moreover, the motivations are used as risk assessment factors for driver–machine interaction in dangerous situations. The system is evaluated on 10 human subjects in a driving simulator. The assistance system had no false activation during the tests. It avoided collisions in all the rear-end crash avoidance scenarios, while 90% of human subjects did not.

scholarly journals User Education in Automated Driving: Owner’s Manual and Interactive Tutorial Support Mental Model Formation and Human-Automation Interaction

Information ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 143 ◽  
Author(s):  
Yannick Forster ◽  
Sebastian Hergeth ◽  
Frederik Naujoks ◽  
Josef Krems ◽  
Andreas Keinath
Keyword(s):  
Conceptual Development ◽  
Method Development ◽  
Driving Simulator ◽  
Driver Assistance ◽  
Automated Driving ◽  
Driver Assistance Systems ◽  
Automation Systems ◽  
Interactive Tutorial ◽  
Model Formation ◽  
User Education

Automated driving systems (ADS) and a combination of these with advanced driver assistance systems (ADAS) will soon be available to a large consumer population. Apart from testing automated driving features and human–machine interfaces (HMI), the development and evaluation of training for interacting with driving automation has been largely neglected. The present work outlines the conceptual development of two possible approaches of user education which are the owner’s manual and an interactive tutorial. These approaches are investigated by comparing them to a baseline consisting of generic information about the system function. Using a between-subjects design, N = 24 participants complete one training prior to interacting with the ADS HMI in a driving simulator. Results show that both the owner’s manual and an interactive tutorial led to an increased understanding of driving automation systems as well as an increased interaction performance. This work contributes to method development for the evaluation of ADS by proposing two alternative approaches of user education and their implications for both application in realistic settings and HMI testing.

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